Subjective Opinions Matter: Controllable Image Quality Assessment Using Pseudo Reference Images

Recently, image quality assessment (IQA) has achieved remarkable progress with the success of deep learning. However, existing IQA methods are practically troublesome. With the strict pre-condition of full-reference (FR) methods limiting its application in real scenarios, the no-reference (NR) scheme is also inconvenient due to its unsatisfying performance and the lack of flexibility or controllability. In this paper, we aim to bridge the gap between FR and NR-IQA and introduce a brand new scheme, namely pseudo-reference image quality assessment (PR-IQA), by introducing pseudo reference images. As the first implementation of PR-IQA, we propose a novel baseline, i.e., Unpaired-IQA, from the perspective of subjective opinion-aware IQA. A self-adaptive feature fusion (SAFF) module is well-designed for the unpaired features in PR-IQA, with which the model can extract quality-discriminative features from distorted images and content variability-robust features from pseudo reference ones, respectively. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art NR-IQA methods, verifying the effectiveness of PR-IQA and demonstrating that a user-friendly, controllable IQA is feasible and successfully realized.